Machine learning based feature engineering for thermoelectric materials by design-Reference-Cited by-同舟云学术

Machine learning based feature engineering for thermoelectric materials by design

Published:2024 Issue:1 Volume:3 Page:210-220
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Vaitesswar U. S.¹^ORCID,Bash Daniil²³^ORCID,Huang Tan¹,Recatala-Gomez Jose⁴,Deng Tianqi⁵⁶⁷^ORCID,Yang Shuo-Wang⁷^ORCID,Wang Xiaonan¹⁸,Hippalgaonkar Kedar³⁴^ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore

2. Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore

3. Institute of Materials Research and Engineering, Agency for Science Technology and Research, 2 Fusionopolis Way, #08-03 Innovis, 138634, Singapore

4. School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Block N4.1, 639798, Singapore

5. State Key Laboratory of Silicon Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China

6. Institute of Advanced Semiconductors, Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices, Hangzhou Innovation Center, Zhejiang University, Hangzhou, Zhejiang 311200, China

7. Institute of High Performance Computing, Agency for Science Technology and Research, 1 Fusionopolis Way, #16-16 Connexis, 138632, Singapore

8. Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China

Abstract

We train several machine learning models on a dataset comprised by Materials Project and calculated thermoelectric power factor. We show that a random forest model outperforms more complex approaches for the dataset and allows for interpretability.

Funder

Institute of Materials Research and Engineering

National Research Foundation Singapore

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/DD/D3DD00131H

Reference59 articles.

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5. Materials Project , https://materialsproject.org/docs/calculations , accessed 30 October, 2018

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